This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts.
This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts.
Carsten Lange is an economics professor at Cal Poly Pomona with a keen interest in making data science and machine learning more accessible. He has authored multiple refereed articles and four books, including his 2004 book on applying neural networks for economics. Carsten is passionate about teaching machine learning and artificial intelligence with a focus on practical applications and hands-on learning.
Inhaltsangabe
1. Introduction 2. Basics of Machine Learning 3. Introduction to R and RStudio 4. k Nearest Neighbors Getting Started 5. Linear Regression Key Machine Learning Concepts 6. Polynomial Regression Overfitting & Tuning Explained 7. Ridge, Lasso, and Elastic Net Regularization Explained 8. Logistic Regression Handling Imbalanced Data 9. Deep Learning MLP Neural Networks Explained 10. Tree Based Models Bootstrapping Explained 11. Interpreting Machine Learning Results 12. Concluding Remarks Index Bibliography
1. Introduction 2. Basics of Machine Learning 3. Introduction to R and RStudio 4. k Nearest Neighbors Getting Started 5. Linear Regression Key Machine Learning Concepts 6. Polynomial Regression Overfitting & Tuning Explained 7. Ridge, Lasso, and Elastic Net Regularization Explained 8. Logistic Regression Handling Imbalanced Data 9. Deep Learning MLP Neural Networks Explained 10. Tree Based Models Bootstrapping Explained 11. Interpreting Machine Learning Results 12. Concluding Remarks Index Bibliography
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